{"id":"W2891585194","doi":"10.48550/arxiv.1906.06172","title":"Deep Learning-Based Decoding of Constrained Sequence Codes","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Decoding methods; Computer science; Convolutional code; Sequential decoding; Algorithm; List decoding; Convolutional neural network; Throughput; Sequence (biology); Concatenated error correction code; Block code; Artificial intelligence; Wireless; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002535145,0.0002560505,0.0003827385,0.0002179769,0.0001165184,0.00007945398,0.001858006,0.000231326,0.00004083202],"category_scores_gemma":[0.00005797902,0.0002795937,0.0001692848,0.0003442434,0.0001678818,0.0003192036,0.001694239,0.0005444201,0.00004176766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001008653,"about_ca_system_score_gemma":0.0003092138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001551403,"about_ca_topic_score_gemma":0.00001394753,"domain_scores_codex":[0.9983485,0.0001550921,0.00021731,0.0008779332,0.0001205305,0.0002806427],"domain_scores_gemma":[0.9979544,0.0002398063,0.0004111781,0.001082098,0.0001958608,0.0001167074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001746145,0.00004675447,0.003437224,0.0001182401,0.00003451593,0.0001098805,0.00007806585,0.964034,0.0002843162,0.03026696,0.00004040113,0.001532135],"study_design_scores_gemma":[0.00039987,0.00006106316,0.0003135834,0.0002094393,0.00002560327,0.000002605009,0.00003604773,0.9946436,0.0006371037,0.003154031,0.0002157262,0.0003013645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0727713,0.00004712556,0.9255355,0.00003038637,0.0003542175,0.0001814408,0.00002137385,0.0001607159,0.0008979103],"genre_scores_gemma":[0.9821587,0.00006500609,0.01741456,0.00003743695,0.00001894613,3.788264e-7,0.00004469773,0.00001122045,0.0002490336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9093874,"threshold_uncertainty_score":0.9999656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07426583864467266,"score_gpt":0.2073462251570728,"score_spread":0.1330803865124001,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}